The overall architecture consists of two main modules, namely feature extraction and classification. In the first module, we initially extract robust micro-emotional visual representations for each individual emotion type. For this purpose, we propose a learnable method which exploits a pooling technique [1]} and a temporal model [2]} rather than using raw emotion features [3]}, [4]} or feature pooling techniques [5]} only to represent a video. Later, these short-time micro-emotional features are aggregated in time and mapped to a single representation for each video. Finally, video-level representations are trained with a linear kernel Support Vector Machine (SVM)LibSVM library. https://www.csie.ntu.edu.tw/~cjlin/libsvm/ for two class prediction problem (i.e. real or fake).
